Abstract
Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural network(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is presented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square (RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level decomposition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance conditions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.
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References
Wang X, Liu C W, Bi F R et al. Fault diagnosis of diesel engine based on adaptive wavelet packets and EEMDfractal dimension[J]. Mechanical Systems and Signal Processing, 2013, 41(1/2): 581–597.
Wang Y, Xu G H, Liang L et al. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis [J]. Mechanical Systems and Signal Processing, 2015, 54–55: 259–276.
Wang Y F, Xue C, Jia X H et al. Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion [J]. Mechanical Systems and Signal Processing, 2015, 56–57: 197–212.
Wang C D, Zhang Y Y, Zhong Z Y. Fault diagnosis for diesel valve trains based on time-frequency images [J]. Mechanical Systems and Signal Processing, 2008, 22(8): 1981–1993.
Liu X F, Bo L, Luo H L. Bearing faults diagnostics based on hybrid LS-SVM and EMD method [J]. Measurement, 2015, 59: 145–166.
Yang B S, Hwang W W, Kim D J et al. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines [J]. Mechanical Systems and Signal Processing, 2005, 19(2): 371–390.
Jegadeeshwaran R, Sugumaran V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines [J]. Mechanical Systems and Signal Processing, 2015, 52–53: 436–446.
Samanta B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms [J]. Mechanical Systems and Signal Processing, 2004, 18(3): 625–644.
Cai G G, Chen X F, He Z J. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox [J]. Mechanical Systems and Signal Processing, 2013, 41(1/2): 34–53.
Wang S B, Huang W G, Zhu Z K. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis [J]. Mechanical Systems and Signal Processing, 2011, 25(4): 1299–1320.
Yu F, Xu X Z. A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network [J]. Applied Energy, 2014, 134: 102–113.
Samanta B, Al-Balushi K R, Al-Araimi S A. Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection [J]. Engineering Applications of Artificial Intelligence, 2003, 16(7/8): 657–665.
Tagliaferri F, Viola I M, Flay R G J. Wind direction forecasting with artificial neural networks and support vector machines [J]. Ocean Engineering, 2015, 97: 65–73.
Shen C Q, Wang D, Kong F R et al. Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier [J]. Measurement, 2013, 46(4): 1551–1564.
Yin G, Zhang Y T, Li Z N et al. Online fault diagnosis method based on incremental support vector data description and extreme learning machine with incremental output structure[J]. Neurocomputing, 2014, 128: 224–231.
Jedliński Ł, Jonak J. Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform [J]. Applied Soft Computing, 2015, 30: 636–641.
Cui H X, Zhang L B, Kang R Y et al. Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method [J]. Journal of Loss Prevention in the Process Industries, 2009, 22(6): 864–867.
Shu G Q, Liang X Y. Identification of complex diesel engine noise sources based on coherent power spectrum analysis [J]. Mechanical Systems and Signal Processing, 2007, 21(1): 405–416.
Si J P, Ren Q S, Liang H B et al. Valve clearance fault detection based on wavelet packet analysis [J]. Journal of Vibration and Shock, 2011, 30(12): 64–68(in Chinese).
Liu Yu, Zhang Junhong, Bi Fengrong et al. Diesel engine valve fault diagnosis based on LMD marginal spectrum [J]. Chinese Internal Combustion Engine Engineering, 2014, 35(6): 96–100(in Chinese).
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Supported by the National Science and Technology Support Program of China (No. 2015BAF07B04).
Bi Fengrong, born in 1965, male, Dr, associate Prof.
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Bi, F., Liu, Y. Fault diagnosis of valve clearance in diesel engine based on BP neural network and support vector machine. Trans. Tianjin Univ. 22, 536–543 (2016). https://doi.org/10.1007/s12209-016-2675-1
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DOI: https://doi.org/10.1007/s12209-016-2675-1